Module 1: Data & Model Pipelines
Build reproducible ingestion, validation, feature, training, and artifact workflows with clear lineage and failure handling.
Course Specialization
EnrollMLOps & AI Deployment
Program Overview
Learn to move models from experiments into controlled services using reproducible pipelines, CI/CD, monitoring, cloud deployment, security, and governance.
Skills & Tools
- Data & Model Pipelines
- Experiment Tracking
- Containers
- CI/CD
- Model Serving
- Monitoring & Drift
- Cloud & Governance
Course Curriculum
Module 2: Experiment Tracking
Record parameters, datasets, code versions, metrics, artifacts, and approvals so experiments can be compared and reproduced.
Module 3: Containerization
Package training and inference workloads with dependable dependencies, runtime configuration, health checks, and resource boundaries.
Module 4: CI/CD for AI Systems
Automate tests, security checks, artifact promotion, staged releases, rollback paths, and environment-specific configuration.
Module 5: Model Serving & APIs
Expose models through validated batch or online interfaces with versioning, concurrency, latency, and capacity considerations.
Module 6: Monitoring & Drift
Observe service health, data quality, model behavior, costs, and changing distributions, then define responsible response workflows.
Module 7: Cloud Deployment
Deploy model services using appropriate managed or container platforms, infrastructure controls, secrets, scaling, and cost visibility.
Module 8: Security & Governance Capstone
Deliver an auditable deployment with access control, supply-chain checks, documentation, approvals, monitoring, and incident considerations.
Capstone Projects
Reproducible Model Delivery Pipeline
Build a tested pipeline that tracks experiments, packages a model service, and promotes an approved artifact between environments.
Monitored AI Service Capstone
Deploy a model API with health, quality, drift, security, and cost signals plus a documented response and rollback process.